Reports
A scan report provides a comprehensive analysis of a GitHub repository, generated after completing a scan. The report is available in the dashboard at /dashboard/scans and can also be downloaded as a PDF.
Overview
Each report is structured into several sections that provide different insights about the repository. The content varies based on the scan level selected:
- Basic - Repository Metadata, Commit Density, Package Bloat
- Standard - Everything in Basic, plus Static Analysis
- Deep - Everything in Standard, plus Code Analysis and Vibe Analysis
Each section includes a score (where applicable) and detailed breakdown of the findings. Scores range from 0 to 100, with higher scores indicating better results.
Scan Summary
The Scan Summary provides a high-level overview of the repository analysis. It appears at the top of every report and includes:
- Scan Level - The level of analysis performed (Basic, Standard, or Deep)
- Scanned At - Timestamp of when the scan was completed
- Overall Scores - Visual score cards for each analysis section:
- Commit Density Score
- Package Bloat Score
- Static Analysis Score (Standard/Deep)
- Code Analysis Score (Deep)
- Vibe Analysis Score (Deep)
The summary helps you quickly assess the overall health and quality of the repository without diving into each section.
Repository Metadata
This section provides general information about the GitHub repository being analyzed. It includes the following metadata:
| Field | Description |
|---|---|
| name | Repository name (e.g., facebook/react) |
| description | Repository description from GitHub |
| stars | Number of GitHub stars |
| forks | Number of forks |
| watchers | Number of watchers |
| language | Primary programming language |
| license | Detected license (e.g., MIT, Apache-2.0) |
| size | Repository size in KB |
| openIssues | Number of open issues |
| contributorsCount | Number of contributors |
| topics | Repository topics/tags |
| createdAt | Repository creation date |
| pushedAt | Last push date |
| lastCommitDate | Date of the last commit |
This metadata is available in all scan levels (Basic, Standard, and Deep).
Commit Density
The Commit Density section analyzes the repository's commit activity to assess development velocity and maintenance health.
Score
A score from 0 to 100 based on the repository's commit activity patterns. Higher scores indicate active, consistent development.
Commits per Month
The average number of commits per month, calculated over the repository's activity history.
Description
A textual assessment of the commit density, describing whether the repository is actively maintained, sporadically updated, or dormant.
Activity History
A visual chart showing commit activity over time, broken down by time buckets (weeks or months). This helps identify:
- Periods of high and low activity
- Recent changes in development pace
- Overall project momentum
Activity Period
The time range covered by the activity analysis, from the first recorded commit to the most recent.
This section is available in all scan levels (Basic, Standard, and Deep).
Package Bloat
The Package Bloat section analyzes the repository's dependencies to identify potential bloat and maintenance concerns.
Score
A score from 0 to 100 based on the dependency profile. Higher scores indicate leaner, well-maintained dependency trees.
Dependency Count
Total number of dependencies found in the repository's package manifests (package.json, etc.).
Dev Dependency Ratio
The ratio of development dependencies to production dependencies. A high ratio may indicate a build-heavy project, while a low ratio suggests a simpler setup.
Description
A textual assessment of the dependency health, describing whether the project has a reasonable dependency footprint or potential bloat issues.
Key Packages
A list of notable dependencies that are central to the project's functionality, helping you understand the technology stack at a glance.
Categorized Dependencies
Dependencies grouped by category (e.g., UI frameworks, testing libraries, build tools). This helps identify:
- What the project is built with
- Potential redundant or conflicting packages
- Outdated dependency categories
Monorepo Packages
If the repository is a monorepo, this shows the internal packages detected within the workspace.
This section is available in all scan levels (Basic, Standard, and Deep).
Static Analysis
The Static Analysis section provides code quality metrics by analyzing the repository's source code without executing it. This section is available in Standard and Deep scan levels.
Score
A score from 0 to 100 based on static analysis findings. Higher scores indicate better code quality, fewer issues, and more maintainable code.
Code Quality Metrics
Detailed metrics about the codebase, which may include:
- Lines of code and file count
- Code complexity scores
- Duplication percentage
- Style and formatting compliance
Issues Found
A breakdown of issues detected, categorized by severity (error, warning, info), helping prioritize what needs attention.
Code Analysis
The Code Analysis section uses AI-powered analysis to provide deep insights into the repository's code quality, architecture, and patterns. This section is only available in Deep scan level.
Score
An AI-generated score from 0 to 100 assessing the overall code quality, architecture, and maintainability.
Architecture Assessment
Analysis of the project's architecture patterns, including:
- Code organization and structure
- Design pattern usage
- Separation of concerns
- Scalability indicators
Code Patterns
Identification of common and notable coding patterns, including both positive patterns (best practices) and anti-patterns that may need attention.
Recommendations
AI-generated suggestions for improving code quality, reducing technical debt, and enhancing maintainability.
Vibe Analysis
The Vibe Analysis section detects "vibe coding" patterns - indicators that the code may have been generated primarily by AI without proper human review or understanding. This section is only available in Deep scan level.
Score
A score from 0 to 100 where higher scores indicate more human-crafted code. Lower scores suggest significant AI-generated content without proper oversight.
Vibe Indicators
Specific patterns detected that suggest AI-generated code, such as:
- Repetitive or templated code structures
- Lack of domain-specific optimizations
- Generic naming and comments
- Over-engineered or under-engineered solutions
Assessment
A qualitative assessment of the code's "vibe", describing the balance between human craftsmanship and AI assistance in the repository.
Context
Important context about the findings, noting that AI-assisted coding is not inherently negative - the analysis focuses on whether the code demonstrates proper understanding and Intentional design.